Dempster-Shafer Evidence Theory (DST). DST, which is a theory on belief functions, was first proposed by Dempster [1]} and is a generalization of the Bayesian theory to subjective probabilities [2]}. Later, it was developed into a general framework to model epistemic uncertainty [3]}. In contrast to Bayesian neural networks, which indirectly obtain uncertainty through multiple stochastic samplings from weight parameters, DST directly models uncertainty. DST allows beliefs from different sources to be combined with various fusion operators to obtain a new belief that considers all available evidence [4]}, [5]}. When faced with beliefs from different sources, Dempster's rule of combination tries to fuse their shared parts, and ignores conflicting beliefs through normalization factors. A more specific implementation will be discussed later.
<FIGURE>